{"id":"W2605475111","doi":"10.1007/978-3-319-57351-9_23","title":"Learning Physical Properties of Objects Using Gaussian Mixture Models","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; WordNet; Taxonomy (biology); Artificial intelligence; Classifier (UML); Inference; Gaussian; Machine learning; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007877875,0.00112763,0.00138589,0.001351404,0.0003103113,0.00149538,0.001735133,0.001528477,0.001678255],"category_scores_gemma":[0.002633021,0.001014589,0.002085404,0.001524609,0.0009554055,0.003329256,0.001701906,0.002142127,0.001436508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005008904,"about_ca_system_score_gemma":0.0003679377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001795934,"about_ca_topic_score_gemma":0.001959917,"domain_scores_codex":[0.9995987,0.00007057915,0.00001909984,0.0001495811,0.0001358589,0.00002629369],"domain_scores_gemma":[0.9991303,0.0004947467,0.00009283175,0.0001656422,0.00008357253,0.00003290341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001611077,0.0001386598,0.002434871,0.0002850648,0.0002415684,0.0001249371,0.0001683437,0.4036212,0.01832483,0.01954957,0.003071734,0.5518782],"study_design_scores_gemma":[0.00000437828,0.00002229642,0.0006620978,0.00001437849,0.00002744841,0.00008225218,0.00001764686,0.9694119,0.002413195,0.02630279,0.001026038,0.00001563012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008447013,0.0003987669,0.989684,0.00006637697,0.00001819629,0.00001467438,0.00006813066,0.0006542595,0.0006487256],"genre_scores_gemma":[0.4134653,0.002572509,0.5752169,0.0001864211,0.0001468576,0.0001511257,0.001480844,0.000549522,0.006230414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001795934,"threshold_uncertainty_score":0.00561434,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03349555143588086,"score_gpt":0.2787326514396241,"score_spread":0.2452371000037432,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}